ScholarWorks@숙명여자대학교

초록

This article is concerned with high frequency financial time series analysis for which partially-quantified prin- cipal component analysis (PCA, for short) is exploited. Partially-quantified PCA is useful especially when the first principal component is subjectively given, for a practical purpose, prior to the usual PCA analysis. High frequency financial time series consists of a lot of intraday returns and thus partially-quantified PCA may help provide a successful dimension reduction for the data. Interesting applications are made to domestic post-Covid- 19 financial data. Specifically, four sets of one-minute high frequency financial data including KOSPI (Korea stock prices index) spanning from January 2022 to July 2024 are analyzed via partially-quantified PCA to illus- trate low-dimensional data reduction for the intraday returns.

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